In order to detect the presence and location of an assemblers hand within an image we train a mobilentv2 [1]} model to output a fixed Sigmoid normalised vector of length 6 containing the elements \([\hat{P}_1,\hat{x}_1,\hat{y}_1,\hat{P}_2,\hat{x}_2,\hat{y}_2]\)  where \(\hat{P}_1\)  and \(\hat{P}_2\)  are the probability of the left and right hand existing in the image (1 if hand present 0 otherwise) and \(\hat{x}_i\) , \(\hat{y}_i\)  are the normalised position of hand i in a given input image. We train with a modified mean squared error loss, shown in equation REF , that doesn't penalise the model for incorrectly predicting a hand position when no hand is present, where \(P_i\)  is the (1,0) label of a present hand and \(x_i\)  and \(y_i\)  are the ground truth normalised location of hand i within a given image. \(\lambda \)  is an equalising constant set to 0.1 to get similar learning rates across the loss function.
\(\mathcal {L} &= \lambda \sum _{i=1}^{2}(P_i-\hat{P}_i)^2 + P_1[(x_{1}-\hat{x}_{1})^2 + (y_{1}-\hat{y}_{1})^2 ] \\&\hspace{50.0pt}+ P_2[(x_{2}-\hat{x}_{2})^2 + (y_{2}-\hat{y}_{2})^2 ] \) 
